Optimizing IoT Data Transmission with the Queen Honey Bee Migration Method for Operational Efficiency of the Hostage Rescue Team
DOI:
https://doi.org/10.22399/ijcesen.3280Keywords:
Data Transmission Optimization, Data Communication, Hostage Rescue, Urban Warfare, QHBMAbstract
This research proposes the Queen Honey Bee Migration (QHBM) algorithm to optimize IoT data transmission in hostage rescue operations, with a primary focus on energy efficiency and reliability. The methodology employs comprehensive simulation techniques to compare QHBM performance against two established optimization algorithms—Fuzzy BT and PSO—across diverse network configurations and operational scenarios. Simulation results demonstrate that QHBM significantly outperforms both alternative approaches. The algorithm extends network lifetime by 25% compared to PSO and 15% compared to Fuzzy BT, addressing a critical requirement for prolonged operation during rescue missions. Additionally, QHBM enhances network throughput by 30%, maintaining a consistent data transmission ratio of 98%, while simultaneously reducing computational overhead by 20%. The QHBM algorithm demonstrates particularly robust performance in challenging environments characterized by high node density and dynamic mobility patterns, which closely resemble real-world hostage rescue scenarios. The algorithm achieves this by dynamically balancing energy consumption across the network while maintaining reliable data transmission pathways, even when network topology changes rapidly. The bio-inspired approach of QHBM leverages the efficient decision-making patterns observed in honey bee colonies, specifically the migration behaviors of queen bees, to create adaptive routing protocols that respond effectively to changing network conditions. This research makes a significant contribution to the development of nature-inspired optimization methods that can enhance the performance and resilience of tactical communication systems deployed in high-stakes rescue operations. The findings suggest promising applications for similar bio-inspired algorithms in other mission-critical IoT deployments where energy efficiency and transmission reliability are paramount concerns.
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